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Record W3007049606 · doi:10.1021/acsnano.9b07671

Surface-Confined Macrocyclization <i>via</i> Dynamic Covalent Chemistry

2020· article· en· W3007049606 on OpenAlexafffund
Chaoying Fu, Jiří Mikšátko, Lea Assies, Vladimír Vrkoslav, Silvia Orlandi, Martin Kalbáč, Petr Kovaříček, Xiaobin Zeng, Boping Zhou, Luca Muccioli, Dmitrii F. Perepichka, Emanuele Orgiu

Bibliographic record

VenueACS Nano · 2020
Typearticle
Languageen
FieldEngineering
TopicSurface Chemistry and Catalysis
Canadian institutionsMcGill UniversityInstitut National de la Recherche Scientifique
FundersGrantová Agentura České RepublikyNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsDynamic covalent chemistryCovalent bondNanotechnologyChemistrySurface (topology)Chemical physicsMaterials scienceMoleculeSupramolecular chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Surface-confined synthesis is a promising approach to build complex molecular nanostructures including macrocycles. However, despite the recent advances in on-surface macrocyclization under ultrahigh vacuum, selective synthesis of monodisperse and multicomponent macrocycles remains a challenge. Here, we report on an on-surface formation of [6 + 6] Schiff-base macrocycles via dynamic covalent chemistry. The macrocycles form two-dimensional crystalline domains on the micrometer scale, enabled by dynamic conversion of open-chain oligomers into well-defined ∼3.0 nm hexagonal macrocycles. We further show that by tailoring the length of the alkyl substituents, it is possible to control which of three possible products—oligomers, macrocycles, or polymers—will form at the surface. In situ scanning tunneling microscopy imaging combined with density functional theory calculations and molecular dynamics simulations unravel the synergistic effect of surface confinement and solvent in leading to preferential on-surface macrocyclization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.189
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2020
Admission routes2
Has abstractyes

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